AI for structural and seismic engineering
Machine-learning methods for structural response prediction, damage classification, building inventory generation, and rapid post-event assessment.
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Data-AI-Resilience LabNational Central UniversityGoogle Scholar ↗Department of Civil Engineering · National Central University
Data-AI-Resilience Laboratory
Data-driven methods for safer and more resilient infrastructure
Artificial IntelligenceEarthquake EngineeringInfrastructure Resilience
OUR APPROACH
From source rupture to urban seismic resilience.
Our research integrates physics-driven AI computational methods and digital twin technologies across the full earthquake engineering chain—from earthquake source rupture and ground-motion simulation to nonlinear structural analysis, urban damage assessment, and resilience-informed decisions.
RESEARCH THEMES
Three connected lines of inquiry
Earthquake risk and regional loss
Physics-based ground motions, fragility analysis, and regional building data to quantify risk across buildings, communities, and cities.
Read more →Infrastructure resilience
Frameworks for understanding how infrastructure systems withstand, adapt to, and recover from hazards—and how analysis can inform action.
Read more →LATEST PUBLICATION
Large-Scale Physics-Based Broadband (up to 8 Hz) Ground-Motion Simulations of Mw 6.4+ Earthquakes for Hualien, Taiwan, Area
W. Zhang, M.-C. Hsieh, P.-Y. Chen · Seismological Research Letters · 2026